docstr: Saves the model at the provided checkpoint.
  
  :param checkpoint: (str) Path to the target checkpoint file.
docstr_tok: ["Saves","the","model","at","the","provided","checkpoint",".",":","param","checkpoint",":","(","str",")","Path","to","the","target","checkpoint","file","."]
code: def save(self, checkpoint):
      """
      Saves the model at the provided checkpoint.
  
      :param checkpoint: (str) Path to the target checkpoint file.
      """
  
      # Some model might need to aggregate variables during checkpointing
      # which requires both the chief and workers to participate in the
      # allreduce communication protocol.
      # So we need to call get_state on every remote workers, otherwise
      # it might get stuck
      state_refs = [w.get_state.remote() for w in self.remote_workers]
  
      state = ray.get(state_refs[0])
  
      with open(checkpoint, "wb") as f:
          pickle.dump(state, f)
  
      return checkpoint
  
imports: import itertools
  import logging
  import pickle
  import numpy
  import ray
  from zoo.common.utils import enable_multi_fs_load, enable_multi_fs_save
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.dl_cluster import RayDLCluster
  from zoo.orca.learn.tf2.tf_runner import TFRunner
  from zoo.orca.learn.ray_estimator import Estimator
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.orca.data.utils import process_spark_xshards
  from zoo.ray import RayContext
label_desc: Use image node_id 7 for calling the TensorFlow2Estimator obj's underlying member method code with example usage: obj.save(checkpoint) and returns: checkpoint